ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation

Ross Callaghan,Daniel C Alexander,Marco Palombo,Hui Zhang

doi:10.1016/j.neuroimage.2020.117107

Abstract

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches.

Highlights

Numerical phantoms play a valuable role in the development and validation of many magnetic resonance imaging (MRI) techniques
Numerical phantoms are often used when developing diffusion MRI microstructure imaging techniques where simulations of the dMRI signal in phantoms with known microstructural properties are used in lieu of an in vivo ground truth measure of microstructure (Alexander et al, 2017)
While recently numerical phantoms have proven useful for validating microstructure imaging of grey matter (Palombo et al, 2020), they have more commonly been used for validating white matter (WM) microstructure, with many studies comparing parameter estimates from fitting their models to the known ground truth from the phantoms e.g. (Daducci et al, 2015; Jelescu and Budde, 2017; Li et al, 2019; Nilsson et al, 2017, 2010; Scherrer et al, 2016; Tariq et al, 2016; Xu et al, 2014; Zhang et al, 2012)

Summary

Introduction

Numerical phantoms play a valuable role in the development and validation of many magnetic resonance imaging (MRI) techniques. Numerical phantoms are often used when developing diffusion MRI (dMRI) microstructure imaging techniques where simulations of the dMRI signal in phantoms with known microstructural properties are used in lieu of an in vivo ground truth measure of microstructure (Alexander et al, 2017). While recently numerical phantoms have proven useful for validating microstructure imaging of grey matter (Palombo et al, 2020), they have more commonly been used for validating white matter (WM) microstructure, with many studies comparing parameter estimates from fitting their models to the known ground truth from the phantoms e.g. Some recent works directly estimate microstructural features using fingerprinting techniques and machine learning to match simulated signals and the corresponding ground truth microstructure of the numerical phantom to the measured signal (Hill et al, 2019; Nedjati-Gilani et al, 2017; Palombo et al, 2018a; Rensonnet et al, 2018). It is important to the MRI community to generate realistic WM numerical phantoms which accurately capture microstructural features in order to get realistic simulated signal

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Discussion

Conclusion